Radiomics supposes an alternative non-invasive tumor characterization tool, which has experienced increased interest with the advent of more powerful computers and more sophisticated machine learning algorithms. Nonetheless, the incorporation of radiomics in cancer clinical-decision support systems still necessitates a thorough analysis of its relationship with tumor biology. Herein, we present a systematic review focusing on the clinical evidence of radiomics as a surrogate method for tumor molecular profile characterization. An extensive literature review was conducted in PubMed, including papers on radiomics and a selected set of clinically relevant and commonly used tumor molecular markers. We summarized our findings based on different cancer entities, additionally evaluating the effect of different modalities for the prediction of biomarkers at each tumor site. Results suggest the existence of an association between the studied biomarkers and radiomics from different modalities and different tumor sites, even though a larger number of multi-center studies are required to further validate the reported outcomes.

Infection with human papilloma virus (HPV) is one of the most relevant prognostic factors in advanced oropharyngeal cancer (OPC) treatment. In this study we aimed to assess the diagnostic accuracy of a deep learning-based method for HPV status prediction in computed tomography (CT) images of advanced OPC. An internal dataset and three public collections were employed (internal: n = 151, HNC1: n = 451; HNC2: n = 80; HNC3: n = 110). Internal and HNC1 datasets were used for training, whereas HNC2 and HNC3 collections were used as external test cohorts. All CT scans were resampled to a 2 mm³ resolution and a sub-volume of 72x72x72 pixels was cropped on each scan, centered around the tumor. Then, a 2.5D input of size 72x72x3 pixels was assembled by selecting the 2D slice containing the largest tumor area along the axial, sagittal and coronal planes, respectively. The convolutional neural network employed consisted of the first 5 modules of the Xception model and a small classification network. Ten-fold cross-validation was applied to evaluate training performance. At test time, soft majority voting was used to predict HPV status. A final training mean [range] area under the curve (AUC) of 0.84 [0.76-0.89], accuracy of 0.76 [0.64-0.83] and F1-score of 0.74 [0.62-0.83] were achieved. AUC/accuracy/F1-score values of 0.83/0.75/0.69 and 0.88/0.79/0.68 were achieved on the HNC2 and HNC3 test sets, respectively. Deep learning was successfully applied and validated in two external cohorts to predict HPV status in CT images of advanced OPC, proving its potential as a support tool in cancer precision medicine.

Synthetic computed tomography (sCT) scans are necessary for dose calculation in magnetic resonance (MR)-only radiotherapy. While deep learning (DL) has shown remarkable performance in generating sCT scans from MR images, research has predominantly focused on high-field MR images. This study presents the first implementation of a DL model for sCT generation in head-and-neck (HN) cancer using low-field MR images. Specifically, the use of vision transformers (ViTs) was explored. The dataset consisted of 31 patients, resulting in 196 pairs of deformably-registered computed tomography (dCT) and MR scans. The latter were obtained using a balanced steady-state precession sequence on a 0.35T scanner. Residual ViTs were trained on 2D axial, sagittal, and coronal slices, respectively, and the final sCTs were generated by averaging the models’ outputs. Different image similarity metrics, dose volume histogram (DVH) deviations, and gamma analyses were computed on the test set (n = 6). The overlap between auto-contours on sCT scans and manual contours on MR images was evaluated for different organs-at-risk using the Dice score. The median [range] value of the test mean absolute error was 57 [37–74] HU. DVH deviations were below 1% for all structures. The median gamma passing rates exceeded 94% in the 2%/2mm analysis (threshold = 90%). The median Dice scores were above 0.7 for all organs-at-risk. The clinical applicability of DL-based sCT generation from low-field MR images in HN cancer was proved. High sCT-dCT similarity and dose metric accuracy were achieved, and sCT suitability for organs-at-risk auto-delineation was shown.

Background: Magnetic Resonance (MR)‐based synthetic Computed Tomography (sCT) generation is an emerging promising technique, required for the transition from conventional planning workflows to MR‐only radiotherapy planning. This shift aims to replace CT acquisition with a sCT improving both cost efficiency and burden to the patient. Generative Adversarial Networks (GANs) have shown some of the best performance in this area. Purpose: This study aims to identify optimal approaches to improve the quality and clinical applicability of MR‐based sCT generation for treatment planning by performing an extensive comparison of GAN architectures and parameters thereof. It focuses on the abdominal region, which still lacks certified medical products for sCT generation. Methods: In order to improve the current state of deep learning technologies, we generated sCTs based on abdominal MR images of 154 cancer patients using GANs, varying the following parameters: (1) generator architectures (U‐Net, ResNet); (2) GAN architectures trained in paired (Pix2Pix) and unpaired fashion (CycleGAN and CUT); (3) number of input‐output channels (2D, 2.5D); (4) training set size. The quality of sCT generation was assessed by using both image similarity and dosimetric metrics; correlation between the two was evaluated. The dosimetric accuracy was evaluated through an automated process that compared the dose distributions of photon treatment plans calculated on sCT and CT images, using Dose‐Volume Histogram (DVH) parameters for tumor and organs at risk. Results: The Pix2Pix model, trained in paired fashion with 2.5D input‐output channels and a ResNet generator emerged as the best‐performing model, achieving a mean absolute error (MAE, mean) of 63.21 HU, a planning target volume Dmean difference of ‐0.09%, and no outliers above 2% for other DVH parameters. This configuration addressed prior challenges of Pix2Pix with bone and rigid organ boundary generation, delivering robust results even for cases with significant air pockets. The 2D input‐output channel configuration showed beneficial for GANs trained in unpaired fashion, achieving a mean MAE of 66.97 HU for CycleGAN and 69.49 HU for CUT. Both delivered clinically applicable results, with mean DVH discrepancies below 0.8%. Expanding the training set size was essential for minimizing outliers in dosimetric parameters. High correlation was observed between the image similarity metrics—MAE, MAE bones, structural similarity index measure—and target DVH parameters, with Pearson coefficients ranging from 0.77 to 0.9. However, within the clinically relevant range of DVH deviations (± 2%), stochastic variations obscured linear trends. Conclusions: The study provided a new benchmark for the abdominal sCT generation task, showing its clinical applicability for treatment planning and further advancing the state‐of‐the‐art. This study also confirmed that image similarity metrics alone can not reliably predict small dosimetric deviations within a clinical threshold; but contributed by identifying specific metrics that correlate with DVH discrepancies above ± 5%, offering valuable tools for training, evaluation, and standardization of reporting across studies.